| % File src/library/stats/man/ls.diag.Rd |
| % Part of the R package, https://www.R-project.org |
| % Copyright 1995-2007 R Core Team |
| % Distributed under GPL 2 or later |
| |
| \name{ls.diag} |
| \title{Compute Diagnostics for \code{lsfit} Regression Results} |
| \usage{ |
| ls.diag(ls.out) |
| } |
| \alias{ls.diag} |
| \arguments{ |
| \item{ls.out}{Typically the result of \code{\link{lsfit}()}} |
| } |
| \description{ |
| Computes basic statistics, including standard errors, t- and p-values |
| for the regression coefficients. |
| } |
| \value{ |
| A \code{list} with the following numeric components. |
| \item{std.dev}{The standard deviation of the errors, an estimate of |
| \eqn{\sigma}.} |
| \item{hat}{diagonal entries \eqn{h_{ii}} of the hat matrix \eqn{H}} |
| \item{std.res}{standardized residuals} |
| \item{stud.res}{studentized residuals} |
| \item{cooks}{Cook's distances} |
| \item{dfits}{DFITS statistics} |
| \item{correlation}{correlation matrix} |
| \item{std.err}{standard errors of the regression coefficients} |
| \item{cov.scaled}{Scaled covariance matrix of the coefficients} |
| \item{cov.unscaled}{Unscaled covariance matrix of the coefficients} |
| } |
| \references{ |
| Belsley, D. A., Kuh, E. and Welsch, R. E. (1980) |
| \emph{Regression Diagnostics.} |
| New York: Wiley. |
| } |
| \seealso{ |
| \code{\link{hat}} for the hat matrix diagonals, |
| \code{\link{ls.print}}, |
| \code{\link{lm.influence}}, \code{\link{summary.lm}}, |
| \code{\link{anova}}. |
| } |
| \examples{ |
| \dontshow{utils::example("lm", echo = FALSE)} |
| ##-- Using the same data as the lm(.) example: |
| lsD9 <- lsfit(x = as.numeric(gl(2, 10, 20)), y = weight) |
| dlsD9 <- ls.diag(lsD9) |
| \donttest{utils::str(dlsD9, give.attr = FALSE)} |
| abs(1 - sum(dlsD9$hat) / 2) < 10*.Machine$double.eps # sum(h.ii) = p |
| plot(dlsD9$hat, dlsD9$stud.res, xlim = c(0, 0.11)) |
| abline(h = 0, lty = 2, col = "lightgray") |
| } |
| \keyword{regression} |